Tech aids sustainable goals

Car manufacturers face a range of global challenges as they strive to move towards sustainable manufacturing. Central to this is ensuring that production processes remain as clean and efficient as possible, while maintaining product quality and reducing wastage.

Digital transformation is underpinning this drive towards sustainable manufacturing, with cloud-based technologies such as artificial intelligence (AI) and machine learning (ML) playing pivotal roles.

Accuracy and timeliness

One area where ML is supporting car manufacturers is in reducing production line interruptions. Automotive industry specialist Richard Felton explains that ML systems can help avoid unplanned maintenance by analysing data to improve predictive maintenance schedules. “If you avoid unnecessary maintenance, you reduce costs, increase productivity, and do not have unplanned downtime,” he says. “ML not only handles the sheer scale, breadth and accuracy of the data, but also the timeliness.”

The technology can also help manufacturers navigate current global component shortages. “Manufacturers are using ML to anticipate shortages and how to handle those shortages in components more efficiently,” he adds.

Efficient component inspections

ML supports component quality inspections, using data from camera inspections to check assembly processes and sequences in terms of complexity, speed, and accuracy. “The machine learning can spot anomalies that human operators might miss across millions of data points,” says Felton.

This digital transformation is supported by companies such as Amazon Web Services (AWS), which as a cloud service provider enables customers to access and manage data, scale globally, and make data-driven decisions in real time using AI, ML, and other advanced services.

AWS offerings help with sustainability, digital manufacturing, and supply chains, as well as improve the overall equipment effectiveness by capturing, analysing, and visualising plant floor data. The services bring this all together as a holistic solution to support the automotive industry.

Felton, who is AWS senior practice manager for Automotive, says the platform has purpose-built capabilities that draw on expertise from across the automotive industry, and offers the “broadest partner ecosystem of any cloud specifically for automotive customers to help them transform their businesses”.

Automating processes with AI

In one example, the company supported a digital production platform for Volkswagen (VW), which has 12 brands operating over more than 120 sites and 1 500 suppliers, with 200 million parts a day entering its factories to produce 11 million cars a year.

“We helped VW tackle that very complex operation with the digital production platform, with analytics in the cloud to help them achieve efficiency, quality, and sustainability,” Felton notes.

During production, VW Group brands apply 25 different labels with over 
2 000 variants containing country-specific safety, usability, and specification data. To automate this process, VW Group brand Porsche developed a solution using the services, which saw manual label inspection replaced with an AI-driven programme to conduct the process automatically, with greater speed and accuracy.

Providing integrated solutions

Manufacturers are increasingly looking for integrated solutions that combine manufacturing systems with those operated by their supply chain partners, to reduce transport costs and lead to more sustainable ways to move millions of parts. In this instance, the digital platform configures route optimisation and ensures correct demand forecast to reduce component waste.

With a trend towards electric vehicles being driven by sustainability goals and emissions regulations, computer-aided engineering can support durability, crash protection, safer use of batteries, and thermal modelling of advance cooling systems.

The services from AWS can help customers predict and understand battery health, capacity, failures, and range, as well as weather conditions impacting battery performance.

The company also works with American electric vehicle automaker, Rivian Automotive, on high-performance computing for design engineering to test crashworthiness, aerodynamics, and durability. “There is a significant time and cost saving by doing simulation in the cloud, as it gives access to a scale that you do not have on present systems; you have almost unlimited capacity to do simulations,” Felton explains.